Another challenge with such end-to-end learning systems is their black-box nature [1]}, [2]}, [3]}. Deep Learning techniques, in general, are highly opaque function approximators having millions of self-learned parameters, each of which might not have any human-interpretable significance. This is a huge setback in life-critical decision-making tasks like autonomous driving, where it is essential to not only arrive at the correct decision but also know how the decision was made by the system.
